The present invention relates to a defect data analyzing method and apparatus and a review system for analyzing defect distribution state from defect data detected by an inspection apparatus in a semiconductor wafer manufacturing process.
In a semiconductor device manufacturing procedure forming a circuit pattern on a semiconductor substrate (hereinafter, referred to a semiconductor wafer manufacturing procedure), a pattern defect inspection or a foreign matter inspection is performed after each process and inspection result is analyzed, so as to improve the yield and stability. As one of such methods, a defect distribution state analysis is known. Most of defect distribution deflection is caused by a device failure or a process error. For this, it is tried to identify a defect cause by identifying a defect distribution pattern characteristic to a device failure or a process error.
There are conventional techniques concerning this as follows. JP-A-10-214866 discloses a method for recognizing a region where defects are concentrated in the defect distribution as a cluster and selecting a point of reviewing (viewing the detected defect by using an optical microscope or a scanning electron microscope) according to the area and shape of the cluster. JP-A-6-61314 discloses a method for dividing wafers into groups according to the state how the defect map is clustered and judging whether the state is similar to a known pattern so as to identify a defect cause. Moreover, JP-A-11-45919 discloses a method for identifying a defect cause by matching defect distribution image data with case database whose defect cause can be identified. U.S. Pat. No. 5,982,920 discloses a method for classifying defects into user-defined events associated with defect causes according to the defect distribution state.
According to the method disclosed in the aforementioned JP-A-11-45919, defect distribution image data is created by using the pixel value of the image data corresponding to the wafer region as a value proportional to the number of defects in the region corresponding to pixels. From the defect distribution image data, a blob of defect concentration, i.e., a cluster is detected. The cluster portion is matched with the case database. According to the information associated with the data of a high matching degree, a defect cause is identified.
According to the method disclosed in U.S. Pat. No. 5,982,920, according to the shape of the defect-concentrated portions, the concentrations of defects are classified into microstructure clusters, curvilinear clusters, and amorphous clusters and the other portions are classified as global. They are respectively classified into user-defined events and related to defect causes.
In the aforementioned methods, it is possible to recognize a cluster when a remarkable defect distribution pattern appears. However, in JP-A-11-45919, no consideration is taken on a case when the defect distribution pattern to be recognized is weak, i.e., when the defect density is low or when the difference between the inside and outside of the patter is small. According to the method disclosed in U.S. Pat. No. 5,982,920, such cases are classified as global. However, various shapes are involved there and should be classified into user-defined events so as to identify a defect cause.